Finance app with scala part 2

closing.scala

}

output.txt

$ scalac finance.scala && scala Main
S&P 500 performance = List((1951,16.3), (1952,11.8), (1953,-6.6), (1954,26.4), (1955,26.4), (1956,2.6), (1957,-14.3), (1958,38.1), (1959,8.5), (1960,-3.0), (1961,23.1), (1962,-11.8), (1963,18.9), (1964,13.0), (1965,9.1), (1966,-13.1), (1967,20.1), (1968,7.7), (1969,-11.4), (1970,0.1), (1971,10.8), (1972,15.6), (1973,-17.4), (1974,-29.7), (1975,31.5), (1976,19.1), (1977,-11.5), (1978,1.1), (1979,12.3), (1980,25.8), (1981,-9.7), (1982,14.8), (1983,17.3), (1984,1.4), (1985,26.3), (1986,14.6), (1987,2.0), (1988,12.4), (1989,27.3), (1990,-6.6), (1991,26.3), (1992,4.5), (1993,7.1), (1994,-1.5), (1995,34.1), (1996,20.3), (1997,31.0), (1998,26.7), (1999,19.5), (2000,-10.1), (2001,-13.0), (2002,-23.4), (2003,26.4), (2004,9.0), (2005,3.0), (2006,13.6), (2007,3.5), (2008,-38.5), (2009,23.5), (2010,12.8))
Arithmetic mean = 8.235000000000001%
Geometric mean = 6.866554380824419%
Actual total growth factor = 53.766085563840456

EIUL performance = List((1951,15.0), (1952,11.8), (1953,0.0), (1954,15.0), (1955,15.0), (1956,2.6), (1957,0.0), (1958,15.0), (1959,8.5), (1960,0.0), (1961,15.0), (1962,0.0), (1963,15.0), (1964,13.0), (1965,9.1), (1966,0.0), (1967,15.0), (1968,7.7), (1969,0.0), (1970,0.1), (1971,10.8), (1972,15.0), (1973,0.0), (1974,0.0), (1975,15.0), (1976,15.0), (1977,0.0), (1978,1.1), (1979,12.3), (1980,15.0), (1981,0.0), (1982,14.8), (1983,15.0), (1984,1.4), (1985,15.0), (1986,14.6), (1987,2.0), (1988,12.4), (1989,15.0), (1990,0.0), (1991,15.0), (1992,4.5), (1993,7.1), (1994,0.0), (1995,15.0), (1996,15.0), (1997,15.0), (1998,15.0), (1999,15.0), (2000,0.0), (2001,0.0), (2002,0.0), (2003,15.0), (2004,9.0), (2005,3.0), (2006,13.6), (2007,3.5), (2008,0.0), (2009,15.0), (2010,12.8))
EIUL arithmetic performance = 8.428333333333335%
EIUL geometric performance = 8.226111328758012%
Actual EIUL total growth factor = 114.79507227304792


  This is where every 10, 15, 20, etc. year interval in all the data is evaluated and then averaged together.
  The min and max performance of each interval is displayed, and the 1st stddev is shown. There is a 68%
  that actual performance is within 1 stddev of the average.
  
10-year stats
==========================
S&P 500 stats:	 Avg geom mean = 7.08 (-3.04..16.06)	68% chance between 2.22 and 11.94
EIUL stats:	 Avg geom mean = 8.08 (5.48..10.34)	68% chance between 6.83 and 9.33

15-year stats
==========================
S&P 500 stats:	 Avg geom mean = 7.30 (0.90..15.59)	68% chance between 3.58 and 11.02
EIUL stats:	 Avg geom mean = 8.18 (6.51..10.54)	68% chance between 7.22 and 9.14

20-year stats
==========================
S&P 500 stats:	 Avg geom mean = 7.16 (2.73..13.95)	68% chance between 4.07 and 10.25
EIUL stats:	 Avg geom mean = 8.13 (6.81..10.16)	68% chance between 7.32 and 8.94

25-year stats
==========================
S&P 500 stats:	 Avg geom mean = 7.15 (3.94..13.05)	68% chance between 4.77 and 9.53
EIUL stats:	 Avg geom mean = 8.18 (7.10..9.82)	68% chance between 7.52 and 8.84

30-year stats
==========================
S&P 500 stats:	 Avg geom mean = 7.17 (5.14..10.05)	68% chance between 5.66 and 8.68
EIUL stats:	 Avg geom mean = 8.20 (7.36..9.00)	68% chance between 7.74 and 8.66