# HG changeset patch
# User Christian Urban <urbanc@in.tum.de>
# Date 1510172876 0
# Node ID 077e63e96287f17d7a0f2a6b4b98507d2f2e172b
# Parent  d58954a96ec17b41e74be1ab5c7299b9fc4a45c6
updated

diff -r d58954a96ec1 -r 077e63e96287 cws/cw01.pdf
Binary file cws/cw01.pdf has changed
diff -r d58954a96ec1 -r 077e63e96287 cws/cw01.tex
--- a/cws/cw01.tex	Wed Nov 08 16:08:16 2017 +0000
+++ b/cws/cw01.tex	Wed Nov 08 20:27:56 2017 +0000
@@ -238,7 +238,8 @@
 useful \texttt{Map} functions: \texttt{.toMap} converts a list of
 pairs into a \texttt{Map}, \texttt{.isDefinedAt(k)} tests whether the
 map is defined at that key, that is would produce a result when
-called with this key.
+called with this key; useful data functions: \texttt{Source.fromURL},
+\texttt{Source.fromFile} for obtaining a webpage and reading a file.
 
 \newpage
 
@@ -274,44 +275,46 @@
 
 \noindent
 Until Yahoo was bought by Altaba this summer, historical stock market
-data was available online for free, but nowadays this kind of data is
-difficult to obtain unless you are prepared to pay extortionate prices
-or be severely rate-limited.  Therefore this coursework comes with a
-number of files containing CSV-lists about stock prices of
-various companies. Use these files for the following tasks.\bigskip
+data for such back-of-the-envelope calculations was available online
+for free, but nowadays this kind of data is difficult to obtain, unless
+you are prepared to pay extortionate prices or be severely
+rate-limited.  Therefore this coursework comes with a number of files
+containing CSV-lists about historical stock prices for companies of
+our portfolio. Use these files for the following tasks.\bigskip
 
 \noindent
 \textbf{Tasks (file drumb.scala):}
 
 \begin{itemize}
 \item[(1.a)] Write a function \texttt{get\_january\_data} that takes a
-  stock symbol and a year as argument. The function reads the
+  stock symbol and a year as arguments. The function reads the
   corresponding CSV-file and returns the list of strings that start
   with the given year (each line in the CSV-list is of the form
-  \texttt{year-01-someday,someprice}.
+  \texttt{year-01-someday,someprice}).
 
 \item[(1.b)] Write a function \texttt{get\_first\_price} that takes
-  again stock symbol and a year as arguments. It should return the
-  first January price for the stock symbol in the year. For this it
-  obtains the list of strings generated by
+  again a stock symbol and a year as arguments. It should return the
+  first January price for the stock symbol in given the year. For this
+  it uses the list of strings generated by
   \texttt{get\_january\_data}.  A problem is that normally a stock
   exchange is not open on 1st of January, but depending on the day of
   the week on a later day (maybe 3rd or 4th). The easiest way to solve
   this problem is to obtain the whole January data for a stock symbol
-  and then select the earliest entry in this list. This entry should
-  be converted into a double.  Such a price might not exist, if the
-  company does not exist in the given year. For example, if you query
-  for Google in January of 1980, then clearly Google did not exists
-  yet.  Therefore you are asked to return a trade price as
-  \texttt{Option[Double]}.
+  and then select the earliest, or first, entry in this list. The
+  stock price of this entry should be converted into a double.  Such a
+  price might not exist, in case the company does not exist in the given
+  year. For example, if you query for Google in January of 1980, then
+  clearly Google did not exists yet.  Therefore you are asked to
+  return a trade price as \texttt{Option[Double]}\ldots\texttt{None}
+  will be the value for when no price exists.
 
 \item[(1.c)] Write a function \texttt{get\_prices} that takes a
   portfolio (a list of stock symbols), a years range and gets all the
-  first trading prices for each year. You should organise this as a
-  list of lists of \texttt{Option[Double]}'s. The inner lists are for
-  all stock symbols from the portfolio and the outer list for the
-  years.  For example for Google and Apple in years 2010 (first line),
-  2011 (second line) and 2012 (third line) you obtain:
+  first trading prices for each year in the range. You should organise
+  this as a list of lists of \texttt{Option[Double]}'s. The inner
+  lists are for all stock symbols from the portfolio and the outer
+  list for the years.  For example for Google and Apple in years 2010
+  (first line), 2011 (second line) and 2012 (third line) you obtain:
 
 \begin{verbatim}
   List(List(Some(311.349976), Some(27.505054)), 
@@ -340,7 +343,8 @@
 \end{verbatim}
 
   That means Google did a bit badly in 2010, while Apple did very well.
-  Both did OK in 2011.\hfill\mbox{[1 Mark]}
+  Both did OK in 2011.\\
+  \mbox{}\hfill\mbox{[1 Mark]}
 
 \item[(3.a)] Write a function that calculates the ``yield'', or
   balance, for one year for our portfolio.  This function takes the
@@ -349,7 +353,7 @@
   unchanged. Otherwise we invest in each existing company an equal
   amount of our balance. Using the change factors computed under Task
   2, calculate the new balance. Say we had \$100 in 2010, we would have
-  received in our running example
+  received in our running example involving Google and Apple:
 
   \begin{verbatim}
   $50 * -0.03573992567129673 + $50 * 0.5399749442411563
@@ -361,7 +365,8 @@
   
 \item[(3.b)] Write a function that calculates the overall balance
   for a range of years where each year the yearly profit is compounded to
-  the new balances and then re-invested into our portfolio.\mbox{}\hfill\mbox{[1 Mark]}
+  the new balances and then re-invested into our portfolio.\\
+  \mbox{}\hfill\mbox{[1 Mark]}
 \end{itemize}\medskip  
 
 \noindent
@@ -370,10 +375,22 @@
 Facebook, Amazon and Baidu; and another for listed real-estate
 companies, whose names I have never heard of. Following the dumb
 investment strategy from 1978 until 2017 would have turned a starting
-balance of \$100 into roughly \$30,895 for real estate and a whopping
-\$188,172 for blue chips.  Note when comparing these results with your
-own results: there might be some small rounding errors, which when
-compounded, lead to moderately different values.\medskip
+balance of \$100 into roughly \$30,839 for real estate and a whopping
+\$349,597 for blue chips.  Note when comparing these results with your
+own calculations: there might be some small rounding errors, which
+when compounded, lead to moderately different values.\bigskip
+
+\noindent
+\textbf{Hints:} useful string functions: \texttt{startsWith(...)} for
+testing a string having a given prefix, \texttt{\_ + \_} for
+concatenating twop strings; useful option functions: \texttt{.flatten}
+flattens a list of options such that it filters way all
+\texttt{None}'s, \texttt{Try(...) getOrElse ...} runs some code that
+might raise an exception, if yes, then a default value can be given;
+useful list functions: \texttt{.head} for obtaining the first element
+in a non-empty list, \texttt{.length} for the length of a
+list.\bigskip
+
 
 \noindent
 \textbf{Moral:} Reflecting on our assumptions, we are over-estimating
@@ -385,8 +402,7 @@
 his inheritance, a really dumb investment strategy would have done
 equally well, if not much better.\medskip
 
-\noindent
-\textbf{Hints:}
+
 \end{document}
 
 %%% Local Variables: 
diff -r d58954a96ec1 -r 077e63e96287 templates/alcohol.csv
--- a/templates/alcohol.csv	Wed Nov 08 16:08:16 2017 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,194 +0,0 @@
-country,beer_servings,spirit_servings,wine_servings,total_litres_of_pure_alcohol
-Afghanistan,0,0,0,0.0
-Albania,89,132,54,4.9
-Algeria,25,0,14,0.7
-Andorra,245,138,312,12.4
-Angola,217,57,45,5.9
-Antigua & Barbuda,102,128,45,4.9
-Argentina,193,25,221,8.3
-Armenia,21,179,11,3.8
-Australia,261,72,212,10.4
-Austria,279,75,191,9.7
-Azerbaijan,21,46,5,1.3
-Bahamas,122,176,51,6.3
-Bahrain,42,63,7,2.0
-Bangladesh,0,0,0,0.0
-Barbados,143,173,36,6.3
-Belarus,142,373,42,14.4
-Belgium,295,84,212,10.5
-Belize,263,114,8,6.8
-Benin,34,4,13,1.1
-Bhutan,23,0,0,0.4
-Bolivia,167,41,8,3.8
-Bosnia-Herzegovina,76,173,8,4.6
-Botswana,173,35,35,5.4
-Brazil,245,145,16,7.2
-Brunei,31,2,1,0.6
-Bulgaria,231,252,94,10.3
-Burkina Faso,25,7,7,4.3
-Burundi,88,0,0,6.3
-Cote d'Ivoire,37,1,7,4.0
-Cabo Verde,144,56,16,4.0
-Cambodia,57,65,1,2.2
-Cameroon,147,1,4,5.8
-Canada,240,122,100,8.2
-Central African Republic,17,2,1,1.8
-Chad,15,1,1,0.4
-Chile,130,124,172,7.6
-China,79,192,8,5.0
-Colombia,159,76,3,4.2
-Comoros,1,3,1,0.1
-Congo,76,1,9,1.7
-Cook Islands,0,254,74,5.9
-Costa Rica,149,87,11,4.4
-Croatia,230,87,254,10.2
-Cuba,93,137,5,4.2
-Cyprus,192,154,113,8.2
-Czech Republic,361,170,134,11.8
-North Korea,0,0,0,0.0
-DR Congo,32,3,1,2.3
-Denmark,224,81,278,10.4
-Djibouti,15,44,3,1.1
-Dominica,52,286,26,6.6
-Dominican Republic,193,147,9,6.2
-Ecuador,162,74,3,4.2
-Egypt,6,4,1,0.2
-El Salvador,52,69,2,2.2
-Equatorial Guinea,92,0,233,5.8
-Eritrea,18,0,0,0.5
-Estonia,224,194,59,9.5
-Ethiopia,20,3,0,0.7
-Fiji,77,35,1,2.0
-Finland,263,133,97,10.0
-France,127,151,370,11.8
-Gabon,347,98,59,8.9
-Gambia,8,0,1,2.4
-Georgia,52,100,149,5.4
-Germany,346,117,175,11.3
-Ghana,31,3,10,1.8
-Greece,133,112,218,8.3
-Grenada,199,438,28,11.9
-Guatemala,53,69,2,2.2
-Guinea,9,0,2,0.2
-Guinea-Bissau,28,31,21,2.5
-Guyana,93,302,1,7.1
-Haiti,1,326,1,5.9
-Honduras,69,98,2,3.0
-Hungary,234,215,185,11.3
-Iceland,233,61,78,6.6
-India,9,114,0,2.2
-Indonesia,5,1,0,0.1
-Iran,0,0,0,0.0
-Iraq,9,3,0,0.2
-Ireland,313,118,165,11.4
-Israel,63,69,9,2.5
-Italy,85,42,237,6.5
-Jamaica,82,97,9,3.4
-Japan,77,202,16,7.0
-Jordan,6,21,1,0.5
-Kazakhstan,124,246,12,6.8
-Kenya,58,22,2,1.8
-Kiribati,21,34,1,1.0
-Kuwait,0,0,0,0.0
-Kyrgyzstan,31,97,6,2.4
-Laos,62,0,123,6.2
-Latvia,281,216,62,10.5
-Lebanon,20,55,31,1.9
-Lesotho,82,29,0,2.8
-Liberia,19,152,2,3.1
-Libya,0,0,0,0.0
-Lithuania,343,244,56,12.9
-Luxembourg,236,133,271,11.4
-Madagascar,26,15,4,0.8
-Malawi,8,11,1,1.5
-Malaysia,13,4,0,0.3
-Maldives,0,0,0,0.0
-Mali,5,1,1,0.6
-Malta,149,100,120,6.6
-Marshall Islands,0,0,0,0.0
-Mauritania,0,0,0,0.0
-Mauritius,98,31,18,2.6
-Mexico,238,68,5,5.5
-Micronesia,62,50,18,2.3
-Monaco,0,0,0,0.0
-Mongolia,77,189,8,4.9
-Montenegro,31,114,128,4.9
-Morocco,12,6,10,0.5
-Mozambique,47,18,5,1.3
-Myanmar,5,1,0,0.1
-Namibia,376,3,1,6.8
-Nauru,49,0,8,1.0
-Nepal,5,6,0,0.2
-Netherlands,251,88,190,9.4
-New Zealand,203,79,175,9.3
-Nicaragua,78,118,1,3.5
-Niger,3,2,1,0.1
-Nigeria,42,5,2,9.1
-Niue,188,200,7,7.0
-Norway,169,71,129,6.7
-Oman,22,16,1,0.7
-Pakistan,0,0,0,0.0
-Palau,306,63,23,6.9
-Panama,285,104,18,7.2
-Papua New Guinea,44,39,1,1.5
-Paraguay,213,117,74,7.3
-Peru,163,160,21,6.1
-Philippines,71,186,1,4.6
-Poland,343,215,56,10.9
-Portugal,194,67,339,11.0
-Qatar,1,42,7,0.9
-South Korea,140,16,9,9.8
-Moldova,109,226,18,6.3
-Romania,297,122,167,10.4
-Russian Federation,247,326,73,11.5
-Rwanda,43,2,0,6.8
-St. Kitts & Nevis,194,205,32,7.7
-St. Lucia,171,315,71,10.1
-St. Vincent & the Grenadines,120,221,11,6.3
-Samoa,105,18,24,2.6
-San Marino,0,0,0,0.0
-Sao Tome & Principe,56,38,140,4.2
-Saudi Arabia,0,5,0,0.1
-Senegal,9,1,7,0.3
-Serbia,283,131,127,9.6
-Seychelles,157,25,51,4.1
-Sierra Leone,25,3,2,6.7
-Singapore,60,12,11,1.5
-Slovakia,196,293,116,11.4
-Slovenia,270,51,276,10.6
-Solomon Islands,56,11,1,1.2
-Somalia,0,0,0,0.0
-South Africa,225,76,81,8.2
-Spain,284,157,112,10.0
-Sri Lanka,16,104,0,2.2
-Sudan,8,13,0,1.7
-Suriname,128,178,7,5.6
-Swaziland,90,2,2,4.7
-Sweden,152,60,186,7.2
-Switzerland,185,100,280,10.2
-Syria,5,35,16,1.0
-Tajikistan,2,15,0,0.3
-Thailand,99,258,1,6.4
-Macedonia,106,27,86,3.9
-Timor-Leste,1,1,4,0.1
-Togo,36,2,19,1.3
-Tonga,36,21,5,1.1
-Trinidad & Tobago,197,156,7,6.4
-Tunisia,51,3,20,1.3
-Turkey,51,22,7,1.4
-Turkmenistan,19,71,32,2.2
-Tuvalu,6,41,9,1.0
-Uganda,45,9,0,8.3
-Ukraine,206,237,45,8.9
-United Arab Emirates,16,135,5,2.8
-United Kingdom,219,126,195,10.4
-Tanzania,36,6,1,5.7
-USA,249,158,84,8.7
-Uruguay,115,35,220,6.6
-Uzbekistan,25,101,8,2.4
-Vanuatu,21,18,11,0.9
-Venezuela,333,100,3,7.7
-Vietnam,111,2,1,2.0
-Yemen,6,0,0,0.1
-Zambia,32,19,4,2.5
-Zimbabwe,64,18,4,4.7
diff -r d58954a96ec1 -r 077e63e96287 templates/alcohol.scala
--- a/templates/alcohol.scala	Wed Nov 08 16:08:16 2017 +0000
+++ b/templates/alcohol.scala	Wed Nov 08 20:27:56 2017 +0000
@@ -13,16 +13,17 @@
   "population.csv"
 
 
-//(1) Complete the get_csv_page function below. It takes the URL-string
-//    as argument and generates a list of strings corresponing to each
-//    line in the csv list. The URL url_alcohol is one possible argument.
+//(1) Complete the get_csv_page function below. It takes a URL-string
+//    as argument and generates a list of strings corresponding to each
+//    line in the downloaded csv-list. The URL url_alcohol above is one 
+//    possible argument.
 
 //def get_csv_page(url: String) : List[String] = ...
 
 
-//    Complete the get_csv_file function below. It takes a file-string
-//    as argument and reads the content of the given file. Like above
-//    it should generates a list of strings corresponing to each
+//    Complete the get_csv_file function below. It takes a file name 
+//    as argument and reads the content of the given file. Like above,
+//    it should generate a list of strings corresponding to each
 //    line in the csv-list. The filename file_population is one possible
 //    argument.
 
@@ -34,7 +35,7 @@
 //    process_alcs extract the country name (as String) and the 
 //    pure alcohol consumption (as Double). For process_pops
 //    generate a Map of Strings (country names) to Long numbers 
-//    (population size). 
+//    (population sizes). 
 
 //def process_alcs(lines: List[String]) : List[(String, Double)] = ...
 
@@ -45,11 +46,11 @@
 //(3) Calculate for each country the overall alcohol_consumption using
 //    the data from the alcohol list and the population sizes list. You
 //    should only include countries on the alcohol list that are also
-//    (with the exact name) on the population sizes list. Note that
+//    on the population sizes list with the exact same name. Note that
 //    the spelling of some names in the alcohol list differs from the
-//    population sizes list. Sort the resulting list according to the 
-//    country with the highest alcohol consumption to the country 
-//    with the lowest alcohol consumption.
+//    population sizes list. You can ignore entries where the names differ. 
+//    Sort the resulting list according to the country with the highest alcohol 
+//    consumption to the country with the lowest alcohol consumption.
 
 //def sorted_country_consumption() : List[(String, Long)] = ...
 
@@ -58,7 +59,7 @@
 //   should be the first element in the tuple below. The second element is
 //   the overall consumption of the first n countries in the sorted list
 //   from above; and finally the double should be the percentage of the 
-//   first n countries of the the world consumption of alcohol.          
+//   first n countries drinking from the the world consumption of alcohol.          
 
 //def percentage(n: Int) : (Long, Long, Double) = ...
 
diff -r d58954a96ec1 -r 077e63e96287 templates/collatz.scala
--- a/templates/collatz.scala	Wed Nov 08 16:08:16 2017 +0000
+++ b/templates/collatz.scala	Wed Nov 08 20:27:56 2017 +0000
@@ -13,7 +13,7 @@
 //def collatz(n: Long) : Long = ...
 
 
-//(2)  Complete the collatz bound function below. It should
+//(2)  Complete the collatz-bound function below. It should
 //     calculate how many steps are needed for each number 
 //     from 1 up to a bound and then calculate the maximum number of
 //     steps and the corresponding number that needs that many 
diff -r d58954a96ec1 -r 077e63e96287 templates/drumb.scala
--- a/templates/drumb.scala	Wed Nov 08 16:08:16 2017 +0000
+++ b/templates/drumb.scala	Wed Nov 08 20:27:56 2017 +0000
@@ -1,4 +1,4 @@
-// Advanvced Part 3 about a really dumb investment strategy
+// Advanced Part 3 about a really dumb investment strategy
 //==========================================================
 
 object CW6c {
@@ -11,68 +11,66 @@
                             "DLR", "EQIX", "EQR", "ESS", "EXR", "FRT", "GGP", "HCP") 
 
 
-// (1.a) The function below takes a stock symbol and a year as argument.
-//       It should read the corresponding CSV-file and read the january 
-//       data from that year. The data should be collected in a list of
-//       strings for each line in the CSV file.
+// (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.
+
+import io.Source
+import scala.util._
 
 //def get_january_data(symbol: String, year: Int) : List[String] = ...
 
-//
-//    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.
-// 
+
 // (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 
-//       given 
-//
+//       generated by get_january_data then the result is None
+
 
 //def get_first_price(symbol: String, year: Int) : Option[Double] = ...
 
 
-// Complete the function below that obtains all first prices
-// for the stock symbols from a portfolio for the given
-// range of years
+// (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]]] = ...
 
-// test case
-//val p = get_prices(List("GOOG", "AAPL"), 2010 to 2012)
+//def get_prices(portfolio: List[String], years: Range) : List[List[Option[Double]]] = ...
+
 
 
 // (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] = ...
+//     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.
 
-//def get_deltas(data: List[List[Option[Double]]]):  List[List[Option[Double]]] = ...
+//def get_delta(price_old: Option[Double], price_new: Option[Double]) : Option[Double] = ...
 
-// test case using the prices calculated above
-//val d = get_deltas(p)
+//def get_deltas(data: List[List[Option[Double]]]) :  List[List[Option[Double]]] = ...
+
 
 
 // (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.
+//     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 = ... 
+//def yearly_yield(data: List[List[Option[Double]]], balance: Long, year: Int) : Long = ... 
+
+//def compound_yield(data: List[List[Option[Double]]], balance: Long, year: Int) : Long = ... 
 
-//test case
-//yearly_yield(d, 100, 0)
+//def investment(portfolio: List[String], years: Range, start_balance: Long) : Long = ...
 
-//def compound_yield(data: List[List[Option[Double]]], balance: Long, year: Int): Long = ... 
 
-//def investment(portfolio: List[String], years: Range, start_balance: Long): Long = ...
 
 //test cases for the two portfolios given above
 
-//investment(rstate_portfolio, 1978 to 2016, 100)
-//investment(blchip_portfolio, 1978 to 2016, 100)
+//investment(rstate_portfolio, 1978 to 2017, 100)
+//investment(blchip_portfolio, 1978 to 2017, 100)
 
 }
diff -r d58954a96ec1 -r 077e63e96287 testing/ESS.csv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/testing/ESS.csv	Wed Nov 08 20:27:56 2017 +0000
@@ -0,0 +1,469 @@
+Date,Adj Close
+1995-01-03,5.852035
+1995-01-04,5.943475
+1995-01-05,5.989194
+1995-01-06,6.080631
+1995-01-09,6.034914
+1995-01-10,6.034914
+1995-01-11,5.989194
+1995-01-12,5.989194
+1995-01-13,6.034914
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diff -r d58954a96ec1 -r 077e63e96287 testing/ESS.cvs
--- a/testing/ESS.cvs	Wed Nov 08 16:08:16 2017 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
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-2008-01-17,65.517059
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-2008-01-22,65.831161
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-2008-01-25,70.207054
-2008-01-28,73.140945
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-2009-01-28,53.492237
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-2010-01-05,65.576508
-2010-01-06,66.034096
-2010-01-07,66.720451
-2010-01-08,66.034096
-2010-01-11,66.081436
-2010-01-12,65.205704
-2010-01-13,66.499565
-2010-01-14,66.949265
-2010-01-15,66.499565
-2010-01-19,67.406853
-2010-01-20,67.604080
-2010-01-21,65.844742
-2010-01-22,63.809284
-2010-01-25,64.258987
-2010-01-26,63.903973
-2010-01-27,64.692902
-2010-01-28,64.235306
-2010-01-29,62.870461
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-2011-01-05,95.334465
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-2011-01-07,93.330894
-2011-01-10,93.084549
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-2011-01-12,93.486900
-2011-01-13,92.986015
-2011-01-14,93.273384
-2011-01-18,94.193069
-2011-01-19,92.238770
-2011-01-20,90.957787
-2011-01-21,91.212357
-2011-01-24,93.084549
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-2011-01-26,94.833580
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-2012-01-11,117.556038
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-2012-01-19,117.861305
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-2017-01-31,219.580872
diff -r d58954a96ec1 -r 077e63e96287 testing/drumb.scala
--- a/testing/drumb.scala	Wed Nov 08 16:08:16 2017 +0000
+++ b/testing/drumb.scala	Wed Nov 08 20:27:56 2017 +0000
@@ -95,7 +95,7 @@
   }
 }
 
-yearly_yield(d, 100, 0)
+//yearly_yield(d, 100, 0)
 //compound_yield(d.take(6), 100, 0)
 
 def investment(portfolio: List[String], years: Range, start_balance: Long): Long = {
diff -r d58954a96ec1 -r 077e63e96287 testing/drumb_test.sh
--- a/testing/drumb_test.sh	Wed Nov 08 16:08:16 2017 +0000
+++ b/testing/drumb_test.sh	Wed Nov 08 20:27:56 2017 +0000
@@ -99,6 +99,9 @@
 if [ $tsts -eq 0 ]
 then
   echo "  yearly_yield(get_deltas(<<GOOG+AAPL 2010 - 2012>>), 100, 0) == 125" >> $out
+  echo "" >> $out
+  echo "  investment(rstate_portfolio, 1978 to 2017, 100) == 30839" >> $out
+  echo "  investment(bchips_portfolio, 1978 to 2017, 100) == 349597" >> $out
   
   if (scala_assert "drumb.scala" "drumb_test3.scala") 
   then
diff -r d58954a96ec1 -r 077e63e96287 testing/drumb_test3.scala
--- a/testing/drumb_test3.scala	Wed Nov 08 16:08:16 2017 +0000
+++ b/testing/drumb_test3.scala	Wed Nov 08 20:27:56 2017 +0000
@@ -34,3 +34,12 @@
 
 
 assert(CW6c.yearly_yield(CW6c.get_deltas(urban_prices), 100, 0) == 125)
+
+
+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") 
+
+
+assert(CW6c.investment(rstate_portfolio, 1978 to 2017, 100) == 30839)
+assert(CW6c.investment(blchip_portfolio, 1978 to 2017, 100) == 349597)